Disturbance of information in superior parietal lobe during dual-task interference in a simulated driving task.

Performing a secondary task while driving causes a decline in driving performance. This phenomenon, called dual-task interference, can have lethal consequences. Previous fMRI studies have looked at the changes in the average brain activity to uncover the neural correlates of dual-task interference. From these results, it is unclear whether the overall modulations in brain activity result from general effects such as task difficulty, attentional modulations, and mental effort or whether it is caused by a change in the responses specific to each condition due to dual-task interference. To overcome this limitation, here, we used multi-voxel pattern analysis (MVPA) to interrogate the change in the information content in multiple brain regions during dual-task interference in simulated driving. Participants performed a lane-change task in a simulated driving environment, along with a tone discrimination task with either short or long onset time difference (Stimulus Onset Asynchrony, SOA) between the two tasks. Behavioral results indicated a robust dual-task effect on lane-change reaction time (RT). MVPA revealed regions that carry information about the driving lane-change direction (shift right/shift left), including the superior parietal lobe (SPL), visual, and motor regions. Comparison of decoding accuracies across SOA conditions in the SPL region revealed lower accuracy in the short compared to the long SOA condition. This change in accuracy was not observed in the visual and motor regions. These findings suggest that the dual-task interference in driving may be related to the disturbance of information processing in the SPL region.

Neural modulation enhancement using connectivity-based EEG neurofeedback with simultaneous fMRI for emotion regulation.

Emotion regulation plays a key role in human behavior and overall well-being. Neurofeedback is a non-invasive self-brain training technique used for emotion regulation to enhance brain function and treatment of mental disorders through behavioral changes. Previous neurofeedback research often focused on using activity from a single brain region as measured by fMRI or power from one or two EEG electrodes. In a new study, we employed connectivity-based EEG neurofeedback through recalling positive autobiographical memories and simultaneous fMRI to upregulate positive emotion. In our novel approach, the feedback was determined by the coherence of EEG electrodes rather than the power of one or two electrodes. We compared the efficiency of this connectivity-based neurofeedback to traditional activity-based neurofeedback through multiple experiments. The results showed that connectivity-based neurofeedback effectively improved BOLD signal change and connectivity in key emotion regulation regions such as the amygdala, thalamus, and insula, and increased EEG frontal asymmetry, which is a biomarker for emotion regulation and treatment of mental disorders such as PTSD, anxiety, and depression and coherence among EEG channels. The psychometric evaluations conducted both before and after the neurofeedback experiments revealed that participants demonstrated improvements in enhancing positive emotions and reducing negative emotions when utilizing connectivity-based neurofeedback, as compared to traditional activity-based and sham neurofeedback approaches. These findings suggest that connectivity-based neurofeedback may be a superior method for regulating emotions and could be a useful alternative therapy for mental disorders, providing individuals with greater control over their brain and mental functions.

EEG-based classification of individuals with neuropsychiatric disorders using deep neural networks: A systematic review of current status and future directions.

The use of deep neural networks for electroencephalogram (EEG) classification has rapidly progressed and gained popularity in recent years, but automatic feature extraction from EEG signals remains a challenging task. The classification of neuropsychiatric disorders demands the extraction of neuro-markers for use in automated EEG classification. Numerous advanced deep learning algorithms can be used for this purpose. In this article, we present a comprehensive review of the main factors and parameters that affect the performance of deep neural networks in classifying different neuropsychiatric disorders using EEG signals. We also analyze the EEG features used for improving classification performance. Our analysis includes 82 scientific journal papers that applied deep neural networks for subject-wise classification based on EEG signals. We extracted information on the EEG dataset and types of disorders, deep neural network structures, performance, and hyperparameters. The results show that most studies have focused on clinical classification, achieving an average accuracy of 91.83 ± 7.34, with convolutional neural networks (CNNs) being the most frequently used network architecture and resting-state EEG signals being the most commonly used data type. Additionally, the review reveals that depression (N = 18), Alzheimer's (N = 11), and schizophrenia (N = 11) were studied more frequently than other types of neuropsychiatric disorders. Our review provides insight into the performance of deep neural networks in EEG classification and highlights the importance of EEG feature extraction in improving classification accuracy. By identifying the main factors and parameters that affect deep neural network performance in EEG classification, our review can guide future research in this area. We hope that our findings will encourage further exploration of deep learning methods for EEG classification and contribute to the development of more accurate and effective methods for diagnosing and monitoring neuropsychiatric disorders using EEG signals.

Attention Modulates Human Visual Responses to Objects by Tuning Sharpening.

Visual stimuli compete with each other for cortical processing and attention biases this competition in favor of the attended stimulus. How does the relationship between the stimuli affect the strength of this attentional bias? Here, we used functional MRI to explore the effect of target-distractor similarity in neural representation on attentional modulation in the human visual cortex using univariate and multivariate pattern analyses. Using stimuli from four object categories (human bodies, cats, cars and houses), we investigated attentional effects in the primary visual area V1, the object-selective regions LO and pFs, the body-selective region EBA, and the scene-selective region PPA. We demonstrated that the strength of the attentional bias towards the target is not fixed but decreases with increasing distractor-target similarity. Simulations provided evidence that this result pattern is explained by tuning sharpening rather than an increase in gain. Our findings provide a mechanistic explanation for behavioral effects of target-distractor similarity on attentional biases and suggest tuning sharpening as the underlying mechanism in object-based attention.

The normalization model predicts responses in the human visual cortex during object-based attention.

Divisive normalization of the neural responses by the activity of the neighboring neurons has been proposed as a fundamental operation in the nervous system based on its success in predicting neural responses recorded in primate electrophysiology studies. Nevertheless, experimental evidence for the existence of this operation in the human brain is still scant. Here, using functional MRI, we examined the role of normalization across the visual hierarchy in the human visual cortex. Using stimuli form the two categories of human bodies and houses, we presented objects in isolation or in clutter and asked participants to attend or ignore the stimuli. Focusing on the primary visual area V1, the object-selective regions LO and pFs, the body-selective region EBA, and the scene-selective region PPA, we first modeled single-voxel responses using a weighted sum, a weighted average, and a normalization model and demonstrated that although the weighted sum and weighted average models also made acceptable predictions in some conditions, the response to multiple stimuli could generally be better described by a model that takes normalization into account. We then determined the observed effects of attention on cortical responses and demonstrated that these effects were predicted by the normalization model, but not by the weighted sum or the weighted average models. Our results thus provide evidence that the normalization model can predict responses to objects across shifts of visual attention, suggesting the role of normalization as a fundamental operation in the human brain.

Dysconnection and cognition in schizophrenia: A spectral dynamic causal modeling study.

Schizophrenia (SZ) is a severe mental disorder characterized by failure of functional integration (aka dysconnection) across the brain. Recent functional connectivity (FC) studies have adopted functional parcellations to define subnetworks of large-scale networks, and to characterize the (dys)connection between them, in normal and clinical populations. While FC examines statistical dependencies between observations, model-based effective connectivity (EC) can disclose the causal influences that underwrite the observed dependencies. In this study, we investigated resting state EC within seven large-scale networks, in 66 SZ and 74 healthy subjects from a public dataset. The results showed that a remarkable 33% of the effective connections (among subnetworks) of the cognitive control network had been pathologically modulated in SZ. Further dysconnection was identified within the visual, default mode and sensorimotor networks of SZ subjects, with 24%, 20%, and 11% aberrant couplings. Overall, the proportion of discriminative connections was remarkably larger in EC (24%) than FC (1%) analysis. Subsequently, to study the neural correlates of impaired cognition in SZ, we conducted a canonical correlation analysis between the EC parameters and the cognitive scores of the patients. As such, the self-inhibitions of supplementary motor area and paracentral lobule (in the sensorimotor network) and the excitatory connection from parahippocampal gyrus to inferior temporal gyrus (in the cognitive control network) were significantly correlated with the social cognition, reasoning/problem solving and working memory capabilities of the patients. Future research can investigate the potential of whole-brain EC as a biomarker for diagnosis of brain disorders and for neuroimaging-based cognitive assessment.

Two distinct networks containing position-tolerant representations of actions in the human brain.

Humans can recognize others' actions in the social environment. This action recognition ability is rarely hindered by the movement of people in the environment. The neural basis of this position tolerance for observed actions is not fully understood. Here, we aimed to identify brain regions capable of generalizing representations of actions across different positions and investigate the representational content of these regions. In a functional magnetic resonance imaging experiment, participants viewed point-light displays of different human actions. Stimuli were presented in either the upper or the lower visual field. Multivariate pattern analysis and a surface-based searchlight approach were employed to identify brain regions that contain position-tolerant action representation: Classifiers were trained with patterns in response to stimuli presented in one position and were tested with stimuli presented in another position. Results showed above-chance classification in the left and right lateral occipitotemporal cortices, right intraparietal sulcus, and right postcentral gyrus. Further analyses exploring the representational content of these regions showed that responses in the lateral occipitotemporal regions were more related to subjective judgments, while those in the parietal regions were more related to objective measures. These results provide evidence for two networks that contain abstract representations of human actions with distinct representational content.

Dynamic functional connectivity estimation for neurofeedback emotion regulation paradigm with simultaneous EEG-fMRI analysis.

Joint Analysis of EEG and fMRI datasets can bring new insight into brain mechanisms. In this paper, we employed the recently introduced Correlated Coupled Tensor Matrix Factorization (CCMTF) method for analysis of the emotion regulation paradigm based on EEG frontal asymmetry neurofeedback in the alpha frequency band with simultaneous fMRI. CCMTF method assumes that the co-variations of the common dimension (temporal dimension) between EEG and fMRI are correlated and not necessarily identical. The results of the CCMTF method suggested that EEG and fMRI had similar covariations during the transition of brain activities from resting states to task (view and upregulation) states and these covariations followed an increasing trend. The fMRI shared spatial component showed activations in the limbic system, DLPFC, OFC, and VLPC regions, which were consistent with the previous studies and were linked to EEG frequency patterns in the range of 1-15 Hz with a correlation value close to 0.75. The estimated regions from the CCMTF method were then used as the candidate nodes for dynamic functional connectivity (dFC) analysis, in which the changes in connectivity from view to upregulation states were examined. The results of the dFC analysis were compared with a Normalized Mutual information (NMI) based approach in two different frequency ranges (1-15 and 15-40 Hz) as the NMI method was applied to the vectors of dFC nodes of EEG and fMRI data. The results of the two methods illustrated that the relation between EEG and fMRI datasets was mostly in the frequency range of 1-15 Hz. These relations were both in the brain activations and the dFCs between the two modalities. This paper suggests that the CCMTF method is a capable approach for extracting the shared information between EEG and fMRI data and can reveal new information about brain functions and their connectivity without solving the EEG inverse problem or analyzing different frequency bands.

Probing fMRI brain connectivity and activity changes during emotion regulation by EEG neurofeedback.

Despite the existence of several emotion regulation studies using neurofeedback, interactions among a small number of regions were evaluated, and therefore, further investigation is needed to understand the interactions of the brain regions involved in emotion regulation. We implemented electroencephalography (EEG) neurofeedback with simultaneous functional magnetic resonance imaging (fMRI) using a modified happiness-inducing task through autobiographical memories to upregulate positive emotion. Then, an explorative analysis of whole brain regions was done to understand the effect of neurofeedback on brain activity and the interaction of whole brain regions involved in emotion regulation. The participants in the control and experimental groups were asked to do emotion regulation while viewing positive images of autobiographical memories and getting sham or real (based on alpha asymmetry) EEG neurofeedback, respectively. The proposed multimodal approach quantified the effects of EEG neurofeedback in changing EEG alpha power, fMRI blood oxygenation level-dependent (BOLD) activity of prefrontal, occipital, parietal, and limbic regions (up to 1.9% increase), and functional connectivity in/between prefrontal, parietal, limbic system, and insula in the experimental group. New connectivity links were identified by comparing the brain functional connectivity between experimental conditions (Upregulation and View blocks) and also by comparing the brain connectivity of the experimental and control groups. Psychometric assessments confirmed significant changes in positive and negative mood states in the experimental group by neurofeedback. Based on the exploratory analysis of activity and connectivity among all brain regions involved in emotion regions, we found significant BOLD and functional connectivity increases due to EEG neurofeedback in the experimental group, but no learning effect was observed in the control group. The results reveal several new connections among brain regions as a result of EEG neurofeedback which can be justified according to emotion regulation models and the role of those regions in emotion regulation and recalling positive autobiographical memories.

Neuroscience20 (BRAIN20, SPINE20, and MENTAL20) Health Initiative: A Global Consortium Addressing the Human and Economic Burden of Brain, Spine, and Mental Disorders Through Neurotech Innovations and Policies.

Neurological disorders significantly impact the world's economy due to their often chronic and life-threatening nature afflicting individuals which, in turn, creates a global disease burden. The Group of Twenty (G20) member nations, which represent the largest economies globally, should come together to formulate a plan on how to overcome this burden. The Neuroscience-20 (N20) initiative of the Society for Brain Mapping and Therapeutics (SBMT) is at the vanguard of this global collaboration to comprehensively raise awareness about brain, spine, and mental disorders worldwide. This paper aims to provide a comprehensive review of the various brain initiatives worldwide and highlight the need for cooperation and recommend ways to bring down costs associated with the discovery and treatment of neurological disorders. Our systematic search revealed that the cost of neurological and psychiatric disorders to the world economy by 2030 is roughly $16T. The cost to the economy of the United States is $1.5T annually and growing given the impact of COVID-19. We also discovered there is a shortfall of effective collaboration between nations and a lack of resources in developing countries. Current statistical analyses on the cost of neurological disorders to the world economy strongly suggest that there is a great need for investment in neurotechnology and innovation or fast-tracking therapeutics and diagnostics to curb these costs. During the current COVID-19 pandemic, SBMT, through this paper, intends to showcase the importance of worldwide collaborations to reduce the population's economic and health burden, specifically regarding neurological/brain, spine, and mental disorders.

Global Data-Driven Analysis of Brain Connectivity During Emotion Regulation by Electroencephalography Neurofeedback.

Background: Emotion regulation by neurofeedback involves interactions among multiple brain regions, including prefrontal cortex and subcortical regions. Previous studies focused on connections of specific brain regions such as amygdala with other brain regions. New method: Electroencephalography (EEG) neurofeedback is used to upregulate positive emotion by retrieving positive autobiographical memories and functional magnetic resonance imaging (fMRI) data acquired simultaneously. A global data-driven approach, group independent component analysis, is applied to the fMRI data and functional network connectivity (FNC) estimated. Results: The proposed approach identified all functional networks engaged in positive autobiographical memories and evaluated effects of neurofeedback. The results revealed two pairs of networks with significantly different functional connectivity among emotion regulation blocks (relative to other blocks of the experiment) and between experimental and control groups (false discovery rate corrected for multiple comparisons, q = 0.05). FNC distribution showed significant connectivity differences between neurofeedback blocks and other blocks, revealing more synchronized brain networks during neurofeedback. Comparison with Existing Methods: Although the results are consistent with those of previous model-based studies, some of the connections found in this study were not found previously. These connections are between (a) occipital and other regions including limbic system/sublobar, prefrontal/frontal cortex, inferior parietal, and middle temporal gyrus and (b) posterior cingulate cortex and hippocampus. Conclusions: This study provided a global insight into brain connectivity for emotion regulation. The brain network interactions may be used to develop connectivity-based neurofeedback methods and alternative therapeutic approaches, which may be more effective than the traditional activity-based neurofeedback methods.

Deep Temporal Organization of fMRI Phase Synchrony Modes Promotes Large-Scale Disconnection in Schizophrenia.

Itinerant dynamics of the brain generates transient and recurrent spatiotemporal patterns in neuroimaging data. Characterizing metastable functional connectivity (FC) - particularly at rest and using functional magnetic resonance imaging (fMRI) - has shaped the field of dynamic functional connectivity (DFC). Mainstream DFC research relies on (sliding window) correlations to identify recurrent FC patterns. Recently, functional relevance of the instantaneous phase synchrony (IPS) of fMRI signals has been revealed using imaging studies and computational models. In the present paper, we identify the repertoire of whole-brain inter-network IPS states at rest. Moreover, we uncover a hierarchy in the temporal organization of IPS modes. We hypothesize that connectivity disorder in schizophrenia (SZ) is related to the (deep) temporal arrangement of large-scale IPS modes. Hence, we analyze resting-state fMRI data from 68 healthy controls (HC) and 51 SZ patients. Seven resting-state networks (and their sub-components) are identified using spatial independent component analysis. IPS is computed between subject-specific network time courses, using analytic signals. The resultant phase coupling patterns, across time and subjects, are clustered into eight IPS states. Statistical tests show that the relative expression and mean lifetime of certain IPS states have been altered in SZ. Namely, patients spend (45%) less time in a globally coherent state and a subcortical-centered state, and (40%) more time in states reflecting anticoupling within the cognitive control network, compared to the HC. Moreover, the transition profile (between states) reveals a deep temporal structure, shaping two metastates with distinct phase synchrony profiles. A metastate is a collection of states such that within-metastate transitions are more probable than across. Remarkably, metastate occupation balance is altered in SZ, in favor of the less synchronous metastate that promotes disconnection across networks. Furthermore, the trajectory of IPS patterns is less efficient, less smooth, and more restricted in SZ subjects, compared to the HC. Finally, a regression analysis confirms the diagnostic value of the defined IPS measures for SZ identification, highlighting the distinctive role of metastate proportion. Our results suggest that the proposed IPS features may be used for classification studies and for characterizing phase synchrony modes in other (clinical) populations.

Dual-Task Interference in a Simulated Driving Environment: Serial or Parallel Processing?

When humans are required to perform two or more tasks concurrently, their performance declines as the tasks get closer together in time. Here, we investigated the mechanisms of this cognitive performance decline using a dual-task paradigm in a simulated driving environment, and using drift-diffusion modeling, examined if the two tasks are processed in a serial or a parallel manner. Participants performed a lane change task, along with an image discrimination task. We systematically varied the time difference between the onset of the two tasks (Stimulus Onset Asynchrony, SOA) and measured its effect on the amount of dual-task interference. Results showed that the reaction times (RTs) of the two tasks in the dual-task condition were higher than those in the single-task condition. SOA influenced the RTs of both tasks when they were presented second and the RTs of the image discrimination task when it was presented first. Results of drift-diffusion modeling indicated that dual-task performance affects both the rate of evidence accumulation and the delays outside the evidence accumulation period. These results suggest that a hybrid model containing features of both parallel and serial processing best accounts for the results. Next, manipulating the predictability of the order of the two tasks, we showed that in unpredictable conditions, the order of the response to the two tasks changes, causing attenuation in the effect of SOA. Together, our findings suggest higher-level executive functions are involved in managing the resources and controlling the processing of the tasks during dual-task performance in naturalistic settings.

Efficient de-noising of high-resolution fMRI using local and sub-band information.

BACKGROUND: High-resolution fMRI, useful for accurate brain mapping, suffers from low functional sensitivity at a reasonable acquisition time. Conventional smoothing techniques although reduce the noise and boost the sensitivity, but degrade the spatial resolution of fMRI. NEW METHODS: We propose a novel spatial de-noising technique to increase sensitivity while preserving the boundaries of active regions in the high-resolution fMRI. A modified version of PCA that utilizes adjacent voxels information (LPCA) is first suggested for de-noising. This technique is then further empowered by its application to wavelet sub-bands (WLPCA). RESULTS: Proposed techniques were assessed on both simulated and experimental data. Identifiablity index was calculated for evaluation of the denoising on the simulated data. Maximum and mean z-scores along with LAE and SSIM were reported on experimental data for two presented techniques as well as Guassian smoothing. WLPCA outperformed other techniques in Identifiablity index, for simulation, and in preserving maximum z-score, for experimental study. COMPARISON WITH EXISTING METHODS: The presented technique was developed to simultaneously suppress the noise and preserve the boundaries of active areas against leakage. For first aim, its achievable mean z-score was compared to conventional Gaussian. For second aim, its maximum z-score was compared to that of no-smoothing. While Gaussian and no-smoothing can work fine with only one measure, WLPCA was able to improve both measures concurrently. CONCLUSIONS: The local PCA based methods, and in particular WLPCA, is an effective noise reduction step that preserves the spatial resolution by preventing activity leakage of high-resolution fMRI data.

Analysis of brain functional connectivity network in MS patients constructed by modular structure of sparse weights from cognitive task-related fMRI.

Cognitive dysfunction in multiple sclerosis (MS) seems to be the result of neural disconnections, leading to a wide range of brain functional network alterations. It is assumed that the analysis of the topological structure of brain connectivity network can be used to assess cognitive impairments in MS disease. We aimed to identify these brain connectivity pattern alterations and detect the significant features for the distinction of MS patients from healthy controls (HC). In this regard, the importance of functional brain networks construction for better exhibition of changes, inducing the improved reflection of functional organization structure should be precisely considered. In this paper, we strove to introduce a framework for modeling the functional connectivity network by considering the two most important intrinsic sparse and modular structures of brain. For the proposed approach, we first derived group-wise sparse representation via learning a common over-complete dictionary matrix from the aggregated cognitive task-based functional magnetic resonance imaging (fMRI) data of all subjects of the two groups to be able to investigate between-group differences. We then applied the modularity concept on achieved sparse coefficients to compute the connectivity strength between the two brain regions. We examined the changes in network topological properties between relapsing-remitting MS (RRMS) and matched HC groups by considering the pairwise connections of regions of the resulted weighted networks and extracting graph-based measures. We found that the informative brain regions were related to their important connectivity weights, which could distinguish MS patients from the healthy controls. The experimental findings also proved the discrimination ability of the modularity measure among all the global features. In addition, we identified such local feature subsets as eigenvector centrality, eccentricity, node strength, and within-module degree, which significantly differed between the two groups. Moreover, these nodal graph measures have been served as the detectors of brain regions, affected by different cognitive deficits. In general, our findings illustrated that integration of sparse representation, modular structure, and pairwise connectivity strength in combination with the graph properties could help us with the early diagnosis of cognitive alterations in the case of MS.

Statistical Significance Assessment of Phase Synchrony in the Presence of Background Couplings: An ECoG Study.

Statistical significance testing is a necessary step in connectivity analysis. Several statistical test methods have been employed to assess the significance of functional connectivity, but the performance of these methods has not been thoroughly evaluated. In addition, the effects of the intrinsic brain connectivity and background couplings on performance of statistical test methods in task-based studies have not been investigated yet. The background couplings may exist independent of cognitive state and can be observed on both pre- and post-stimulus time intervals. The background couplings may be falsely detected by a statistical test as task-related connections, which can mislead interpretations of the task-related functional networks. The aim of this study was to investigate the relative performance of four commonly used non-parametric statistical test methods-surrogate, demeaned surrogate, bootstrap resampling, and Monte Carlo permutation methods-in the presence of background couplings and noise, with different signal-to-noise ratios (SNRs). Using simulated electrocorticographic (ECoG) datasets and phase locking value (PLV) as a measure of functional connectivity, we evaluated the performances of the statistical test methods utilizing sensitivity, specificity, accuracy, and receiver operating curve (ROC) analysis. Furthermore, we calculated optimal p values for each statistical test method using the ROC analysis, and found that the optimal p values were increased by decreasing the SNR. We also found that the optimal p value of the bootstrap resampling was greater than that of other methods. Our results from the simulation datasets and a real ECoG dataset, as an illustrative case report, revealed that the bootstrap resampling is the most efficient non-parametric statistical test for identifying the significant PLV of ECoG data, especially in the presence of background couplings.

Predicting Blood Oxygenation Level-Dependent Activity in Fusiform Face Area from the Activity in Other Visual Areas.

Neuroimaging studies have shown that discrete regions in ventral visual pathway respond selectively to specific object categories. For example, the fusiform face area (FFA) in humans is consistently more responsive to face than nonface images. However, it is not clear how other cortical regions contribute to this preferential response in FFA. To address this question, we performed a functional magnetic resonance imaging study on human subjects watching naturalistic movie clips from human actions. We then used correlation and multivariate regression (partial least-squares regression) analyses to estimate/predict the mean BOLD (blood oxygenation level-dependent) activity in FFA, from the mean and pattern of responses in 24 visual cortical areas. Higher tier retinotopic areas V3, hV4, and LO2, motion-selective area middle temporal, body-selective areas, and non-FFA face-selective areas had the best prediction accuracy particularly when they were located ipsilateral to FFA. All non-FFA collectively could explain up to 75% of variance in the FFA response. The regression models were also designed to predict the mean activity in one face area from the pattern of activity in another face area. The prediction power was significantly higher between the occipital face area and FFA. The multivariate regression analysis provides a new framework for investigating functional connectivity between cortical areas, and it could inform hierarchical models of visual cortex.

Comparison of statistical tests in effective connectivity analysis of ECoG data.

BACKGROUND: The effects of statistical testing on the results of multivariate autoregressive (MVAR)-based effective connectivity analysis have not been adequately investigated, and it is still unclear which statistical test can provide the most accurate results. NEW METHODS: Using simulated and real electrocorticographic (ECoG) data, we investigated the performance of three nonparametric statistical tests - Monte Carlo permutation, bootstrap resampling, and surrogate data method in MVAR-based effective connectivity analysis. Receiver operating characteristic (ROC) analysis and area under the ROC curve (AUC) were used to assess the performance of each statistical test method. In addition, we found optimal p-values for each method based on ROC analysis. Finally, we investigated the application of statistical tests on partial directed coherence analysis of ECoG data collected in a patient with epilepsy. RESULTS: The bootstrap statistical test performed more accurately than other methods. The surrogate method slightly outperformed the Monte Carlo permutation method. Optimal p-values of statistical tests depended on signal-to-noise ratio (SNR) of data, and its value increased by reducing SNR of data. By considering the typical SNR range of electrophysiological data, we recommended an optimal p-value range for the application of each statistical test method. COMPARISON WITH EXISTING METHODS: Limited studies have investigated the performance of statistical tests for MVAR-based effective connectivity analysis. For the first time, we have investigated the effects of baseline connections on the various performances of statistical tests. CONCLUSIONS: We recommend utilizing the bootstrap statistical test with p-value between 0.05 and 0.1 for effective connectivity analysis of ECoG data.

Nonlinear effective connectivity measure based on adaptive Neuro Fuzzy Inference System and Granger Causality.

Exploring brain networks is an essential step towards understanding functional organization of the brain, which needs characterization of linear and nonlinear connections based on measurements like EEG or MEG. Conventional measures of connectivity are mostly linear and bivariate. This paper proposes an effective connectivity measure called Adaptive Neuro-Fuzzy Inference System Granger Causality (ANFISGC). The proposed measure is based on the symplectic geometry embedding dimension, Adaptive Neuro-Fuzzy Inference System (ANFIS) predictor, and Granger Causality (GC). It is a powerful predictor that detects both linear and nonlinear causal information flow. It is not bivariate and thus can distinguish between direct and indirect connections. The performance of the proposed method is evaluated and compared with those of the Linear Granger Causality (LGC), Kernel Granger Causality (KGC), combination of Pairwise Granger Causality and Conditional Granger Causality (PwGC + CGC), Transfer Entropy (TE), and Phase Transfer Entropy (PTE) methods using simulated and experimental MEG data. Simulation results show that ANFISGC outperforms the other methods in detecting both linear and nonlinear connections and, by increasing the coupling strength between nodes, the value of ANFISGC increases. In the analysis of the time series of the brain sources of epilepsy patients obtained from the MEG inverse problem, the regions found by ANFISGC were more similar to the clinical findings than those found by the other methods.

Distinct Functional Network Connectivity for Abstract and Concrete Mental Imagery.

In several behavioral psycholinguistic studies, it has been shown that concrete words are processed more efficiently. They can be remembered faster, recognized better, and can be learned easier than abstract words. This fact is called concreteness effect. There are fMRI studies which compared the neural representations of concrete and abstract concepts in terms of activated regions. In the present study, a comparison has been made between the condition-specific connectivity of functional networks (obtained by group ICA) during imagery of abstract and concrete words. The obtained results revealed that the functional network connectivity between three pairs of networks during concrete imagery is significantly different from that of abstract imagery (FDR correction at the significance level of 0.05). These results suggest that abstract and concrete concepts have different representations in terms of functional network connectivity pattern. Remarkably, in all of these network pairs, the connectivity during concrete imagery is significantly higher than that of abstract imagery. These more coherent networks include both linguistic and visual regions with a higher engagement of the right hemisphere, so the results are in line with dual coding theory. Additionally, these three pairs of networks include the contrasting regions which have shown stronger activation either in concrete or abstract word processing in former studies. The findings imply that the brain is more integrated and synchronized at the time of concrete imagery and it may explain the reason of faster concrete words processing. In order to validate the results, we used functional network connectivity distributions (FNCD). Wilcoxon rank-sum test was used to check if the abstract and concrete FNCDs extracted from whole subjects are the same. The result revealed that the corresponding distributions are different which indicates two different patterns of connectivity for abstract and concrete word processing. Also, the mean of FNCD is significantly higher at the time of concrete imagery than that of abstract imagery. Furthermore, FNCDs at the single-subject level are significantly more left-skewed or equally, include more strong connectivity for concrete imagery.